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While atomically precise metal nanoclusters (NCs) with unique structures and reactivity are very promising in catalysis, the spatial resistance caused by the surface ligands and structural instability poses significant challenges. In this work, Au25(Cys)18 NCs are encapsulated in multivariate metal–organic frameworks (MOFs) to afford Au25@M-MOF-74 (M = Zn, Ni, Co, Mg). By the MOF confinement, the Au25 NCs showcase highly enhanced activity and stability in the intramolecular cascade reaction of 2-nitrobenzonitrile. Notably, the interaction between the metal nodes in M-MOF-74 and Au25(Cys)18 is able to suppress the free vibration of the surface ligands on the Au25 NCs and thereby improve the accessibility of Au sites; meanwhile, the stronger interactions lead to higher electron density and core expansion within Au25(Cys)18. As a result, the activity exhibits the trend of Au25@Ni-MOF-74 > Au25@Co-MOF-74 > Au25@Zn-MOF-74 > Au25@Mg-MOF-74, highlighting the crucial roles of microenvironment modulation around the Au25 NCs by interaction between the surface ligands and MOF hosts. [ABSTRACT FROM AUTHOR]

The phenomenon of flexoelectricity, wherein mechanical deformation induces alterations in the electron configuration of metal oxides, has emerged as a promising avenue for regulating electron transport. Leveraging this mechanism, stress sensing can be optimized through precise modulation of electron transport. In this study, the electron transport in 2D ultra‐smooth In2O3 crystals is modulated via flexoelectricity. By subjecting cubic In2O3 (c‐In2O3) crystals to significant strain gradients using an atomic force microscope (AFM) tip, the crystal symmetry is broken, resulting in the separation of positive and negative charge centers. Upon applying nano‐scale stress up to 100 nN, the output voltage and power values reach their maximum, e.g. 2.2 mV and 0.2 pW, respectively. The flexoelectric coefficient and flexocoupling coefficient of c‐In2O3 are determined as ≈0.49 nC m−1 and 0.4 V, respectively. More importantly, the sensitivity of the nano‐stress sensor upon c‐In2O3 flexoelectric effect reaches 20 nN, which is four to six orders smaller than that fabricated with other low dimensional materials based on the piezoresistive, capacitive, and piezoelectric effect. Such a deformation‐induced polarization modulates the band structure of c‐In2O3, significantly reducing the Schottky barrier height (SBH), thereby regulating its electron transport. This finding highlights the potential of flexoelectricity in enabling high‐performance nano‐stress sensing through precise control of electron transport. [ABSTRACT FROM AUTHOR]

Amorphous carbon (a-C) has promising potential for temperature sensing due to its outstanding properties. In this work, an a-C thin film temperature sensor integrated with the MEMS silicon accelerometer was proposed, and a-C film was deposited on the fixed frame of the accelerometer chip. The a-C film was deposited by DC magnetron sputtering and linear ion beam, respectively. The nanostructures of two types of films were observed by SEM and TEM. The cluster size of sp2 was analyzed by Raman, and the content of sp2 and sp3 of the carbon film was analyzed by XPS. It showed that the DC-sputtered amorphous carbon film, which had a higher sp2 content, had better temperature-sensitive properties. Then, an integrated sensor chip was designed, and the structure of the accelerometer was simulated and optimized to determine the final sizes. The temperature sensor module had a sensitivity of 1.62 mV/°C at the input voltage of 5 V with a linearity of 0.9958 in the temperature range of 20~150 °C. The sensitivity of the sensor is slightly higher than that of traditional metal film temperature sensors. The accelerometer module had a sensitivity of 1.4 mV/g/5 V, a nonlinearity of 0.38%, a repeatability of 1.56%, a total thermomechanical noise of 509 μg over the range of 1 to 20 Hz, and an average thermomechanical noise density of 116 µg/√Hz, which is smaller than the input acceleration amplitude for testing sensitivity. Under different temperatures, the performance of the accelerometer was tested. This research provided significant insights into the convenient procedure to develop a high-performance, economical temperature–accelerometer-integrated MEMS sensor. [ABSTRACT FROM AUTHOR]

In recent years, the development of environmentally friendly, lead-free ferroelectric films with prominent electrostrictive effects have been a key area of focus due to their potential applications in micro-actuators, sensors, and transducers for advanced microelectromechanical systems (MEMS). This work investigated the enhanced electrostrictive effect in lead-free sodium bismuth titanate-based relaxor ferroelectric films. The films, composed of (Bi0.5Na0.5)0.8−xBaxSr0.2TiO3 (BNBST, x = 0.02, 0.06, and 0.11), with thickness around 1 μm, were prepared using a sol-gel method on Pt/TiO2/SiO2/Si substrates. By varying the Ba2+ content, the crystal structure, morphology, and electrical properties, including dielectric, ferroelectric, strain, and electromechanical performance, were investigated. The films exhibited a single pseudocubic structure without preferred orientation. A remarkable strain response (S > 0.24%) was obtained in the films (x = 0.02, 0.06) with the coexistence of nonergodic and ergodic relaxor phases. Further, in the x = 0.11 thick films with an ergodic relaxor state, an ultrahigh electrostrictive coefficient Q of 0.32 m4/C2 was achieved. These findings highlight the potential of BNBST films as high-performance, environmentally friendly electrostrictive films for advanced microelectromechanical systems (MEMS) and electronic devices. [ABSTRACT FROM AUTHOR]

A MEMS piezoresistive sensor for measuring accelerations greater than 100,000 g (about 106 m/s2) is described in this work. To enhance the performance of the sensor, specifically widening its measurement range and natural frequency, a cross-beam construction with a center block was devised, and a Wheatstone bridge was formed by placing four piezoresistors at the ends of the fixed beams to convert acceleration into electricity. The location of the varistor was determined using the finite element approach, which yielded the optimal sensitivity. Additionally, a reliable Pt-Ti-Pt-Au electrode was designed to solve the issue of the electrode failing under high impact and enhancing the stability of the ohmic contact. The accelerometer was fabricated using MEMS technology, and the experiment with a Hopkinson pressure bar and hammering was conducted, and the bias stability was measured. It had a sensitivity of 1.06 μV/g with good linearity. The simulated natural frequency was 633 kHz The test result revealed that the accelerometer can successfully measure an acceleration of 100,000 g. [ABSTRACT FROM AUTHOR]

The process of developing new drugs is widely acknowledged as being time‐intensive and requiring substantial financial investment. Despite ongoing efforts to reduce time and expenses in drug development, ensuring medication safety remains an urgent problem. One of the major problems involved in drug development is hepatotoxicity, specifically known as drug‐induced liver injury (DILI). The popularity of new drugs often poses a significant barrier during development and frequently leads to their recall after launch. In silico methods have many advantages compared with traditional in vivo and in vitro assays. To establish a more precise and reliable prediction model, it is necessary to utilize an extensive and high‐quality database consisting of information on drug molecule properties and structural patterns. In addition, we should also carefully select appropriate molecular descriptors that can be used to accurately depict compound characteristics. The aim of this study was to conduct a comprehensive investigation into the prediction of DILI. First, we conducted a comparative analysis of the physicochemical properties of extensively well‐prepared DILI‐positive and DILI‐negative compounds. Then, we used classic substructure dissection methods to identify structural pattern differences between these two different types of chemical molecules. These findings indicate that it is not feasible to establish property or substructure‐based rules for distinguishing between DILI‐positive and DILI‐negative compounds. Finally, we developed quantitative classification models for predicting DILI using the naïve Bayes classifier (NBC) and recursive partitioning (RP) machine learning techniques. The optimal DILI prediction model was obtained using NBC, which combines 21 physicochemical properties, the VolSurf descriptors and the LCFP_10 fingerprint set. This model achieved a global accuracy (GA) of 0.855 and an area under the curve (AUC) of 0.704 for the training set, while the corresponding values were 0.619 and 0.674 for the test set, respectively. Moreover, indicative substructural fragments favorable or unfavorable for DILI were identified from the best naïve Bayesian classification model. These findings may help prioritize lead compounds in the early stage of drug development pipelines. [ABSTRACT FROM AUTHOR]